xarray.Coordinate

class xarray.Coordinate(name, data, attrs=None, encoding=None, fastpath=False)

Wrapper around pandas.Index that adds xarray specific functionality.

The most important difference is that Coordinate objects must always have a name, which is the dimension along which they index values.

Coordinates must always be 1-dimensional. In addition to Variable methods and properties (attributes, encoding, broadcasting), they support some pandas.Index methods directly (e.g., get_indexer), even though pandas does not (yet) support duck-typing for indexes.

__init__(name, data, attrs=None, encoding=None, fastpath=False)

Methods

__init__(name, data[, attrs, encoding, fastpath])
all([dim, axis, keep_attrs])	Reduce this Variable’s data by applying all along some dimension(s).
any([dim, axis, keep_attrs])	Reduce this Variable’s data by applying any along some dimension(s).
argmax([dim, axis, skipna, keep_attrs])	Reduce this Variable’s data by applying argmax along some dimension(s).
argmin([dim, axis, skipna, keep_attrs])	Reduce this Variable’s data by applying argmin along some dimension(s).
argsort([axis, kind, order])	Returns the indices that would sort this array.
astype(dtype[, order, casting, subok, copy])	Copy of the array, cast to a specified type.
broadcast_equals(other)	True if two Variables have the values after being broadcast against each other; otherwise False.
chunk([chunks, name, lock])	Coerce this array’s data into a dask arrays with the given chunks.
clip([min, max, out])	Return an array whose values are limited to [min, max].
concat(variables[, dim, positions, shortcut])	Specialized version of Variable.concat for Coordinate variables.
conj()	Complex-conjugate all elements.
conjugate()	Return the complex conjugate, element-wise.
copy([deep])	Returns a copy of this object.
count([dim, axis, keep_attrs])	Reduce this Variable’s data by applying count along some dimension(s).
equals(other)	True if two Variables have the same dimensions and values; otherwise False.
expand_dims(dims[, shape])	Return a new variable with expanded dimensions.
fillna(value)
get_axis_num(dim)	Return axis number(s) corresponding to dimension(s) in this array.
identical(other)	Like equals, but also checks attributes.
isel(**indexers)	Return a new array indexed along the specified dimension(s).
isnull(*args, **kwargs)	Detect missing values (NaN in numeric arrays, None/NaN in object arrays)
item(*args)	Copy an element of an array to a standard Python scalar and return it.
load()	Manually trigger loading of this variable’s data from disk or a remote source into memory and return this variable.
load_data()
max([dim, axis, skipna, keep_attrs])	Reduce this Variable’s data by applying max along some dimension(s).
mean([dim, axis, skipna, keep_attrs])	Reduce this Variable’s data by applying mean along some dimension(s).
median([dim, axis, skipna, keep_attrs])	Reduce this Variable’s data by applying median along some dimension(s).
min([dim, axis, skipna, keep_attrs])	Reduce this Variable’s data by applying min along some dimension(s).
notnull(*args, **kwargs)	Replacement for numpy.isfinite / -numpy.isnan which is suitable for use on object arrays.
prod([dim, axis, skipna, keep_attrs])	Reduce this Variable’s data by applying prod along some dimension(s).
reduce(func[, dim, axis, keep_attrs, allow_lazy])	Reduce this array by applying func along some dimension(s).
roll(**shifts)	Return a new Variable with rolld data.
round(*args, **kwargs)
searchsorted(v[, side, sorter])	Find indices where elements of v should be inserted in a to maintain order.
shift(**shifts)	Return a new Variable with shifted data.
squeeze([dim])	Return a new Variable object with squeezed data.
stack(**dimensions)	Stack any number of existing dimensions into a single new dimension.
std([dim, axis, skipna, keep_attrs])	Reduce this Variable’s data by applying std along some dimension(s).
sum([dim, axis, skipna, keep_attrs])	Reduce this Variable’s data by applying sum along some dimension(s).
to_coord()	Return this variable as an xarray.Coordinate
to_index()	Convert this variable to a pandas.Index
to_variable()	Return this variable as a base xarray.Variable
transpose(*dims)	Return a new Variable object with transposed dimensions.
unstack(**dimensions)	Unstack an existing dimension into multiple new dimensions.
var([dim, axis, skipna, keep_attrs])	Reduce this Variable’s data by applying var along some dimension(s).
where(cond)

Attributes

T
attrs	Dictionary of local attributes on this variable.
chunks	Block dimensions for this array’s data or None if it’s not a dask array.
data
dims	Tuple of dimension names with which this variable is associated.
dtype
encoding	Dictionary of encodings on this variable.
imag
name
nbytes
ndim
real
shape
size
values	The variable’s data as a numpy.ndarray